script to make plot now accepts CSV and uses argparse

Former-commit-id: 8c5054036c3241d207364d5e9a139b14fd697057

scripts_dataset_and_results/make_pr_plot.py

@@ -2,20 +2,38 @@
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 import pandas as pd
+import argparse
+
+# Parse command-line arguments
+parser = argparse.ArgumentParser(
+    description='Make a Precision and Recall plot from the CSV result',
+    formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument('csv',
+                    help='CSV file with the precision and recall results.')
+parser.add_argument('out',
+                    help='Output plot image.')
+parser.add_argument('--title',
+                    default='',
+                    help='Title of the plot in the figure.')
+args = parser.parse_args()
 
 # Data extraction
-data = pd.read_excel("/Users/dguera/Desktop/test.xlsx")
-pr_crowd = data["Precision (crowd)"].as_matrix(range(len(data["Precision (crowd)"])))
-r_crowd = data["Recall (crowd)"].as_matrix(range(len(data["Recall (crowd)"])))
-r = data["r"].as_matrix(range(len(data["r"])))
+df = pd.read_csv(args.csv)
+precision = df.precision.values
+recall = df.recall.values
+r = df.r.values
 
-#  Create the figure for "Crowd" Dataset
+# Create the figure for "Crowd" Dataset
+plt.ioff()
 fig, ax = plt.subplots()
-precision = ax.plot(r, pr_crowd, 'r--',label='Precision')
-recall = ax.plot(r, r_crowd,  'b:',label='Recall')
+precision = ax.plot(r, precision, 'r--',label='Precision')
+recall = ax.plot(r, recall,  'b:',label='Recall')
 ax.legend()
-ax.set_ylabel('Percentatge')
+ax.set_ylabel('%')
 ax.set_xlabel(r'$r$ (in pixels)')
 ax.grid(True)
-fig.savefig('/Users/dguera/Desktop/plot.eps')
-#plt.show()
+plt.title(args.title)
+
+# Save to disk
+fig.savefig(args.out)
+